Apr

05

2021

Python Fast Track for Data Science and Machine Learning

Laser 5 Apr 2021 14:16 LEARNING » e-learning - Tutorial

Python Fast Track for Data Science and Machine Learning
MP4 | Video: h264, 1280x720 | Audio: AAC, 48000 Hz
Language: English | Size: 5.03 GB | Duration: 12h 33m

Basic syntax in Python and learn how to use it for data analysis.

What you'll learn

Practical use of Numpy and pandas, learning how to handle data, especially how to deal with null values, imbalanced data, and so on.

Data visualization techniques with matplotlib and Seaborn as well as other advanced visualization tools.

Pre-processing techniques for binary classification, multi-class classification, and regression.

Scikit-learn basics with supervised and unsupervised algorithms and modules

8 key hands-on practices for supervised and unsupervised data analytics tasks

Image classification techniques with TensorFlow

Basic concepts and practices for anomaly detection, GANs, and NLP.

Requirements

No prior experience is required. This course is for absolute bners, so it might be too easy for someone already familiar with data science and machine learning techniques.

The only things you need are an internet connection and a Google account since this course will use the Google Colab website, which is free to use. You do not have to install anything. The only things you have to do are (1) our ipynb notebooks and upload them to your Google Colab account (2) the data we provide and upload it to your Google Colab account, (3) work on those notebooks, and (4) watch the videos to learn more.

Description

The course is the easiest training you can get to start your journey as a data scientist. We help you prepare your portfolio by having you do tabular data classifications, image classifications, and more during eight mini-projects. Learning Python, NumPy, pandas, matplotlib, Seaborn, TensorFlow, or PyTorch is good, but working on real projects for data science is essential.

A lot of classes talk about mathematics and advanced statistics, but those things are not easy for an absolute bner to learn. This course won't teach you math or statistics. Instead, we'll guide you through practical applications.

We are not saying math and statistics are not important. It is imperative that you understand those concepts to become a data scientist. But when you first learn how to drive, you don't need someone to teach you the mechanical details of how your car runs. What you need is someone who can sit next to you and guide you from your home to school or the grocery store. If you get familiar with that, then you will want to go further. In the same way, after you do our projects step-by-step, you will want to know more about the math or statistics behind all that code.

We will start with Python, and then NumPy & pandas for some data manipulation. Then we will learn data visualization, preprocessing, machine learning, and modeling for binary classification with tabular data. Next, we will learn regression and multi-class classification with scikit-learn modules. After that, we'll learn how to deal with unsupervised learning tasks like image classification, image generation, and so on.

Who this course is for:

Absolute bners who are interested in data science or becoming data scientists



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